System and Method for Employing Decision Activities for Providing Inferences in a Process Execution Environment

• US 20080288433A1
• Filed: 02/05/2008
• Published: 11/20/2008
• Est. Priority Date: 05/18/2007
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: BEA Systems Incorporated (Oracle Corporation)
• Inventors: Nores, Juan Cruz, Rodriguez Olivera, Fernando Javier, Lopez-Gabeiras, Emilio
• Granted Patent: US 7,890,448 B2
• Time in Patent Office: Days
• US Class Current: 706/47
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/06316 Sequencing of tasks or work
• CPC Class Codes: G06Q 10/0633 Workflow analysis

Abstract

A set of decision activities can be provided to a business process management and design software tool. The tool can be used to define various processes that contain one or more activities linked by transitions. During process design time, a business analyst can identify process-relevant data of the process to be presented to the end users. The relevant data can be rendered to the end users at runtime, along with a set of possible options for the activities in a process instance. Upon receiving a decision of the user, the process instance can follow the selected transition to a different activity. Over time, the system can capture the decisions taken by users and enter the data associated with these decisions into a data classifier. In doing so, the software tool can generate suggestions, provide probabilities, automate and distribute workloads, flag abnormalities and deviations and generally improve process design and management.

Claims

1. A method for providing inferences in a process execution environment, said method comprising:

repeatedly executing a process that includes a set of activity points, said process having a set of variables associated therewith, wherein at least one activity point requires a decision to be made upon a process instance reaching said activity point;

determining a decision upon reaching said activity point, the decision made employing one or more variables from the set of variables;

continuously collecting a set of values for the process, the set of values based on the one or more variables; and

formulating an inference upon next execution of the process instance, said inference derived from the collected set of values for said process.

2. The method of claim 1 wherein formulating the inference upon the next execution of the process instance further includes:

inferring a correlation present between the decision made at said activity point and the one or more variables relevant to making the decision.

3. The method of claim 1 wherein formulating the inference upon the next execution of the process instance further includes:

inferring, upon next execution of the process instance, a standard path to choose for said process instance; and

providing said standard path as a suggestion when formulating the decision upon the next execution of the process instance.

4. The method of claim 3 wherein the standard path is based on a path in the process most frequently selected by one or more experts.

5. The method of claim 3, further comprising:

detecting a deviation from the standard path by the process instance during the next execution of the process instance; and

issuing an alert for the process instance upon detecting said deviation.

6. The method of claim 1, further comprising:

correlating one or more relevant variables with the decision such that the one or more relevant variables influence the decision to be made.

7. The method of claim 1 wherein formulating the inference upon next execution of the process instance further includes:

feeding the collected set of values into a support vector machine using a radial basis function kernel.

8. The method of claim 1, further comprising:

persisting the collected set of values and the formulated inference in a content repository.

9. The method of claim 1, further comprising:

distributing a workload of said process instance based on process instance-specific content according to said inference.

10. The method of claim 1 wherein repeatedly executing the process further includes:

determining that a sufficient amount of data has been collected during the repeated execution of the process; and

formulating the inference upon the determination that the sufficient amount of data has been collected.

11. A computer-readable medium carrying one or more sequences of instructions for providing inferences in a process execution environment, which instructions, when executed by one or more processors, cause the one or more processors to carry out the steps of:

repeatedly executing a process that includes a set of activity points, said process having a set of variables associated therewith, wherein at least one activity point requires a decision to be made upon a process instance reaching said activity point;

determining a decision upon reaching said activity point, the decision made employing one or more variables from the set of variables;

continuously collecting a set of values for the process, the set of values based on the one or more variables; and

formulating an inference upon next execution of the process instance, said inference derived from the collected set of values for said process.

12. The computer-readable medium of claim 11 wherein the instructions for formulating the inference upon the next execution of the process further include instructions for carrying out the step of:

inferring a correlation present between the decision made and the one or more variables relevant to making the decision.

13. The computer-readable medium of claim 11 wherein the instructions for formulating the inference upon the next execution of the process further include instructions for carrying out the steps of:

inferring, upon next execution of the process, a standard path to choose for said process instance; and

providing said standard path as a suggestion when formulating the decision upon the next execution of the process.

14. The computer-readable medium of claim 13 wherein the standard path is based on a path in the process most frequently selected by one or more experts.

15. The computer-readable medium of claim 13, further comprising instructions for carrying out the steps of:

detecting a deviation from the standard path by the process instance during the next execution of the process instance; and

issuing an alert for the process instance upon detecting said deviation.

16. The computer-readable medium of claim 11, further comprising instructions for carrying out the step of:

correlating one or more relevant variables with the decision such that the one or more relevant variables influence the decision to be formulated.

17. The computer-readable medium of claim 11 wherein the instructions for formulating the inference upon next execution of the process further include instructions for carrying out the step of:

feeding the collected set of values into a support vector machine using a radial basis function kernel.

18. The computer-readable medium of claim 11, further comprising instructions for carrying out the step of:

persisting the collected set of values and the inference formulated in a content repository.

19. The computer-readable medium of claim 11, further comprising instructions for carrying out the step of:

distributing a workload of said process instance based on process instance-specific content according to said inference.

20. The computer-readable medium of claim 11 wherein instructions for repeatedly executing the process further include instructions for carrying out the steps of:

determining that a sufficient amount of data has been collected during the repeated execution of the process; and

formulating the inference upon the determination that the sufficient amount of data has been collected.